Time series covering up to four decades reveals major changes and drivers of marine growth and proportion of repeat spawners in an Atlantic salmon population

Abstract Wild Atlantic salmon populations have declined in many regions and are affected by diverse natural and anthropogenic factors. To facilitate management guidelines, precise knowledge of mechanisms driving population changes in demographics and life history traits is needed. Our analyses were conducted on (a) age and growth data from scales of salmon caught by angling in the river Etneelva, Norway, covering smolt year classes from 1980 to 2018, (b) extensive sampling of the whole spawning run in the fish trap from 2013 onwards, and (c) time series of sea surface temperature, zooplankton biomass, and salmon lice infestation intensity. Marine growth during the first year at sea displayed a distinct stepwise decline across the four decades. Simultaneously, the population shifted from predominantly 1SW to 2SW salmon, and the proportion of repeat spawners increased from 3 to 7%. The latter observation is most evident in females and likely due to decreased marine exploitation. Female repeat spawners tended to be less catchable than males by anglers. Depending on the time period analyzed, marine growth rate during the first year at sea was both positively and negatively associated with sea surface temperature. Zooplankton biomass was positively associated with growth, while salmon lice infestation intensity was negatively associated with growth. Collectively, these results are likely to be linked with both changes in oceanic conditions and harvest regimes. Our conflicting results regarding the influence of sea surface temperature on marine growth are likely to be caused by long‐term increases in temperature, which may have triggered (or coincided with) ecosystem shifts creating generally poorer growth conditions over time, but within shorter datasets warmer years gave generally higher growth. We encourage management authorities to expand the use of permanently monitored reference rivers with complete trapping facilities, like the river Etneelva, generating valuable long‐term data for future analyses.


| INTRODUC TI ON
Wild Atlantic salmon (Salmo salar) face a complex suite of environmental stressors throughout their lives. Some of these stressors are natural, while others are caused by constantly expanding anthropogenic activities in rivers and the coastal zone (Forseth et al., 2017;Lennox et al., 2021). With some exceptions in the northern areas (Niemelä et al., 2005), Atlantic salmon (hereon referred to as salmon) populations have declined throughout most of their distribution over the past several decades (Friedland et al., 2009;Jensen et al., 2011;Peyronnet et al., 2007Peyronnet et al., , 2008Todd et al., 2008). Parasites like salmon lice Lepeophtheirus salmonis (Thorstad et al., 2015) and Gyrodactylus salaris (Johnsen & Jensen, 1991), introgression of escaped domesticated salmon (Bolstad et al., 2017;Fleming et al., 2000;Glover et al., 2013Glover et al., , 2019McGinnity et al., 2003;Skaala et al., 2019), river regulations and agriculture practices have all been identified as major threats to the abundance of salmon populations, although their relative importance varies from region to region and over time (Forseth et al., 2017).
It is also becoming increasingly evident that climate change, by influencing physical and biological conditions in both the freshwater and marine phase of the salmon´s anadromous life cycle, is likely to directly and indirectly influence survival, production, and distribution of wild salmon populations (Beaugrand & Reid, 2003;Friedland et al., 2009;Jensen et al., 2011;Tréhin et al., 2021). It is, therefore, necessary to investigate a diverse range of factors, from direct anthropogenic to climatic, in order to identify and quantify the mechanisms underpinning variation in growth and population abundance in salmon (Chaput, 2012;ICES, 2013;NASCO, 2002NASCO, , 2009. In order to elucidate some of these processes, earlier studies have investigated, with contrasting results, correlations between angling catch reports, marine return rates or post-smolt growth, and climate variables such as sea surface temperatures (SST) and the North Atlantic Oscillation (NAO) index, and the biomass of pelagic fish species (Bacon et al., 2009;Beaugrand & Reid, 2003;Friedland et al., 2000;Jensen et al., 2012;Quinn et al., 2006;Todd et al., 2008;Utne et al., 2020). Other studies (Brett, 1979;Friedland et al., 2000, and references therein) found marine growth rate, particularly during the post-smolt period, to be correlated with sea temperature and prey abundance. As marine growth rate and survival are partially linked (Friedland et al., 2000;Jonsson et al., 2003), environmental factors affecting marine growth rate, caused by either human activities or natural variations, represent key elements in our understanding of variations in population abundance, and ultimately, how to manage these populations.
The overall aim of the present study was to investigate temporal variation in marine growth rate of salmon during their first year at sea, age at maturation, the proportion of repeat spawners in the population, and finally, to identify potential drivers of variation in marine growth. These analyses were conducted on a unique dataset from the river Etneelva using the following three sources of data: (a) angling reports and scale samples covering four decades, (b) extensive sampling of the whole spawning run from 2013 onwards in an upstream migration trap, and finally (c) an environmental time series of sea surface temperature, zooplankton biomass, and sea lice intensity spanning up to four decades.

| Study design
The study consisted of two datasets: (1) salmon captured during the angling season (mid-June to mid-August) for intermittent years 1983 to 2019, with date of capture and biological measurements for each fish, (2) salmon captured in the upstream migration trap, with date of capture and biological measurements for each individual fish entering from April to November (2013 to 2019). The angling data were collected by the Institute of Marine Research (IMR) and the Norwegian Institute for Nature Research (NINA; Table 1). In addition, measurements of sea surface temperature, biomass of zooplankton, and median salmon lice intensity were also compiled from various sources for the various years in the study period (Supplementary data for more information). Average marine growth for the trap and angling datasets are presented (Table S1).

| The river Etneelva
The river Etneelva is located near the mouth of the Hardangerfjord on the west coast of Norway (Figure 1). The anadromous section is within shorter datasets warmer years gave generally higher growth. We encourage management authorities to expand the use of permanently monitored reference rivers with complete trapping facilities, like the river Etneelva, generating valuable long-term data for future analyses.

K E Y W O R D S
Atlantic salmon, marine growth, salmon lice, sea temperature, veteran spawners, zooplankton

T A X O N O M Y C L A S S I F I C A T I O N
Evolutionary ecology 13 km, covering ~290 000 m 2 of habitat. In 2013, a resistance board weir fish trap was installed in the lower part of the river to monitor and sample the spawning runs for salmon and anadromous brown trout (Salmo trutta) (Harvey et al., 2017;Madhun et al., 2017;Quintela et al., 2016;Skaala et al., 2015). The trap is also used to remove putative escaped domesticated salmon (Madhun et al., 2017). For each fish that enters the trap, the species (salmon or trout), sex, length, and weight were recorded. A small number of scales were taken from each fish for age and growth analyses (sampled above the lateral line between the dorsal and adipose fin), and a micro-clip was taken from the tip of the adipose fin for genetics, before wild fish were released above the trap. Based on sub-sampling methods and snorkeling counts, the catch efficiency of the trap has been estimated at approximately 98% for escaped domesticated salmon and slightly less for wild salmon (Skoglund et al., 2021).
The study was conducted in agreement with the Vestland County Governor, the Norwegian Environmental Agency and the Norwegian Food Safety Authority with permits (No. 2015/34273-1 and No. 19/36679/-1) to capture, sample, and tag salmon.

| Age and growth analyses
For determination of age and growth, rinsed scales were photographed with calibration using a stereomicroscope. The number of years in freshwater until smoltification, the number of winters in the TA B L E 1 Number of wild salmon for each sampling method (trap and angling) used in the analyses pertaining to this study from the river Etneelva from 1983 to 2019. The total number of salmon caught by angling and ascending the trap are shown for each year, and the number of salmon divided into sexes, spawning status, and sea ages for each year are also shown. analyzed; therefore, the number of repeat spawners in those years are lower (and not representative) than in other years where all fish scales were analyzed (see Table 1). In addition, smolt length was back-calculated for a subset of individuals captured in the trap and all angled individuals using the methodology described by Lea-Dahl (Dahl, 1910;Lea, 1910; Table S1).

| Marine growth during the first year at sea
All statistics were carried out using R v4.1.2 (R Core Team, 2016).
Generalized linear models were used to investigate variations in marine growth during the first year of the fish caught by angling and a subset of the fish caught in the trap. The response variable was marine growth, measured as the post-smolt growth increment (PGI), and calculated by subtracting the back-calculated smolt length from the estimated length after the first winter at sea.
Marine growth was modeled using a Gaussian distribution with a log-link function using glmmTMB function from the glmmTMB package in R (Brooks et al., 2017) in all models unless stated otherwise.
As certain variables of interest were present in different subsets of years, it was decided to investigate marine growth using different models, depending on the availability of the data. The analyses were, therefore, split into demographic and environmental models for each dataset, that is, two models for the angling dataset and two models for the trap dataset. In the demographic model for the angling data, smolt year classes (ranging from 1980 to 2018) were grouped into decades, modeled as an explanatory variable consisting of four levels (80s, 90s, 00s, and 10s). The other explanatory variables included in the model were the sex (two levels: male or female), and sea age (three levels: 1, 2 or multi-sea winter (MSW)) of each fish, with a two-way interaction for sex and decade. All variables were modeled as categorical variables, and decade was included in the dispersion formula to account for heteroscedasticity. above, and the model was fitted using a Gaussian distribution with a log-link function using glmmTMB as above.
Model fits were assessed by using the DHARMa package in R (Hartig, 2022). The Anova function from the car package (Fox & Weisberg, 2019) was used to assess the significance of the explanatory variables for the glm models, and anova.gam was used to assess the significance of the smooth terms for the gam models. For the significant main categorical variables with more than three levels and for significant two-way interactions, pairwise comparisons between each level of the factor were carried out using the pairs function from the emmeans (estimated marginal means) package (Lenth, 2016) with the default Tukey adjustment for multiple comparisons.

| Age at maturation
A series of two-proportion Z tests were used to investigate the difference in proportions of salmon of each sea age between the decades of angling and between the years of capture in the trap to explore potential shifts in age at maturation over time. p values were adjusted for multiple comparisons using a Bonferroni correction.

| Sea residency of repeat spawners
Two-proportion Z tests were used to assess differences in the proportion of repeat spawners observed in historical (1983 + 1984) and contemporary (2018 + 2019) angling samples, between sexes within the trap and angling samples. 2018 and 2019 were used as these represented the most contemporary samples that contained complete estimation of repeat spawners.

| Marine growth during the first year of the salmon captured by angling
Marine growth to the first annual zone, that is, at the completion of the first summer and winter at sea, was significantly associated with decade and sea age, while neither sex nor its two-way interaction with decade was significantly associated with marine growth (Table 2A:  (Table S2).
In the environmental models, the results for the relationship between marine growth during the first year at sea and average summer sea surface temperature differed depending on the model  (Table 2b). Marine growth significantly increased with zooplankton biomass values and SST in a nonlinear manner (Figure 3a, Table 2d). In the model containing only summer SST as a covariate (here, the entire study period of smolt year classes 1980-2018), the relationship between marine growth and the average summer SST was linear, significant, and negative (F value = 6.06, estimated df = 1, p value = .012; Figure 3a).

| Marine growth during the first year of salmon captured in the trap
Marine growth was significantly different between the smolt year classes (2012-2016), sea ages, and sexes (  Figure 2C), and in 2013 1 SW fish also had significantly higher growth than 2 SW fish (Table 3) (Table 2D; Figure 3b).

| Age at maturation
The proportion of 1SW salmon caught by angling decreased significantly between the 1980s and 2010s. The proportion of 1SW was lower in the 80s than in the 90s albeit this difference was not significant. The proportion of 1SW was significantly lower in the 2010s than in every other decade, dropping from 0.70 in the 90s to 0.15 (Table 3; Figure 2c). The opposite trend was observed in 2SW fish, with significantly higher proportions of 2SW fish caught by angling in the 2000s and 2010s than in the previous two decades (Table 3; Figure 2c). The proportion of MSW salmon caught by angling was significantly higher in the 2010s compared with every other decade, while there were no differences in proportions observed between the 80s, 90s, and 2000s (Table 3; Figure 2c).
The proportion of 1SW fish caught in the trap varied over the years without a clear trend (Table 3; Figure 2d). The proportion of  (Table 3; Figure 2d).

| Proportion of repeat spawners
The total proportion of repeat spawners in the population was significantly lower in the historical (1983 & 1984) angling samples (3%) compared with the contemporary (2018 & 2019) angling samples (7%) ( Table 4A). Within sexes, there were significantly lower proportions of female repeat spawners in the historical angling samples (2%) compared with the contemporary angling samples (9%) (Table 4A). This trend was also evident for the males, but statistically not significant (historical: 3% and contemporary: 6%) (Table 4A).
The total proportion of repeat spawners in the contemporary samples (2018 + 2019) was significantly lower in those captured by angling (8%) compared to those ascending the trap in the same years (10%) ( Table 4B). There were significantly less females in the angling (8%) than in the trap samples (14%), but no difference between proportions of males in the angling (8%) and the trap samples (6%) ( Table 4B). The proportion of repeat spawners fluctuated among the years, although in 2014, a year with low salmon returns, the relative number of repeat spawners was high (Table 1) Zooplankton positively influenced marine growth, while sea lice intensity negatively influenced growth. This is the first study to investigate the combined influence of SST, zooplankton biomass, and sea lice intensity on marine growth in salmon. We conclude that both changing oceanic conditions over time and anthropogenic activities have contributed to these clear changes in the population demography and age structure.

| Marine growth rate and age at maturation
A very clear decline in marine growth in the first year at sea was observed over the smolt year classes from 1980 to 2018. A similar temporal reduction in marine growth has also been reported in several other long-term studies of Atlantic salmon populations in the Northeast Atlantic (Bacon et al., 2009;Fiske et al., 2008;Peyronnet et al., 2007;Smith et al., 2007;Todd et al., 2008).
The observed temporal reduction in growth rate for fish of all age groups during the first year at sea was accompanied by a temporal  catches in 59 Norwegian rivers over a 15-year period and reported an overall increase in the age at maturity from 1SW to 2SW fish.
In the present study, marine growth in the first year at sea was statistically associated with the subsequent age at maturation; however,

| Environmental drivers of marine growth
Growth rate in fish is closely linked with temperature, and with increasing sea temperatures during the last decades, it could be ex- through climate changes influencing prey availability (Jonsson et al., 2016;Todd et al., 2008Todd et al., , 2020. In the present study, marine growth fell to an all-time low for the smolt year classes around 2007, just as zooplankton abundance dropped sharply from a high level at about 10-15 g/m 2 down to about half the biomass ( Figure 3A). A drop in marine growth being correlated with a decrease in zooplankton availability has also been observed by others (Beaugrand & Reid, 2003;Friedland et al., 2009;Todd et al., 2008). Jensen et al. (2012) identified associations between biomass of pelagic fishes (SSB), zooplankton biomass, and growth rate in salmon.
In the trap dataset, we also observed a significant and negative effect of sea lice intensity on marine growth. The potential negative effects from salmon lice on marine growth and survival of anadromous salmonid species have been debated for several decades, particularly in relation to areas with high density of salmon farming (Grimnes & Jakobsen, 1996;Krkosek et al., 2007;Shephard & Gargan, 2021;Skilbrei & Wennevik, 2006;Vollset et al., 2018).
Although our data did not allow for a full study on the impact from salmon lice on the survival of salmon, we have expanded existing knowledge on drivers, including salmon lice, of marine growth in a naturally recruited salmon population.

| The proportion of repeat spawners
The striking increase observed in the proportion of repeat spawners in the population through the period from 1980 to 2018 is most likely caused by a reduction in mortality of fish following their first spawning event. This could occur in the river or the sea, or a combination.
By 1984, Norwegian salmon were heavily exploited upon their migratory return to the coastline, with 21 210 drift nets, 1 697 bag nets, and 35 lift nets in operation in the Norwegian home water fishery (Hansen, 1988 in 1986, including a total ban of drift net fisheries (Hansen, 1988), in combination with a relatively low estimated angling mortality in the river Etneelva compared with other studies (Borgstrøm et al., 2010;Erkinaro et al., 1999;Hansen, 1990), an increase in the proportion of repeat spawners in wild salmon populations in this area, and especially the river Etneelva, would be expected. Similar increases in repeat spawners have been observed in Canada due in part to size restrictions on the recreational fishery (Reid & Chaput, 2012). Erkinaro et al. (2019) examined four decades of scale samples from salmon fisheries in the Teno River in northern Europe. The authors found an increase in repeat spawners over time, which they attribute to changes in both fishery exploitation and environmental conditions. Repeat spawners are of particular importance in years with low maiden return, for example, in 2014 where low returns of salmon were observed but a high proportion of repeat spawners relative to other years, and the drivers behind observed spatio-temporal changes have been addressed by a number of studies (Bordeleau et al., 2020;Hansen, 1988;Peyronnet et al., 2007).
Most of the repeat spawners identified in this study returned as alternate spawners, that is, two years after the previous spawning, as opposed to consecutive spawners the year after. However, this differed between the sexes, as males more often than females tended to return as consecutive spawners. The positive association between female size and fecundity, egg size and energy content (Bordleau et al., 2020;Fleming, 1996), may suggest that egg quality is affected by reconditioning strategy (Reid & Chaput, 2012). In turn, this may explain why an alternative strategy was more commonly observed in females than in males.
The underrepresentation of female repeat spawners relative to males in the angling catches compared with their overrepresentation in the trap suggests intersexual differences in behaviors and therefore angling catchability. This would be in accordance with behavioral differences observed between males and females during the spawning season in salmon (Fleming, 1996) and in anadromous brown trout (Johnsson et al., 2001), where males spend relatively more energy in aggressive contests with other males cruising up and down the river, looking for spawning opportunities, while females use energy in selecting and defending spawning sites.

| Management Implications
Our study revealed that changes in marine growth in the first year at sea and in the age and spawning structure of the population have occurred due to changes in oceanic conditions and anthropogenic activities. Determining such changes and their drivers and elucidating how these processes and activities influence salmon populations is key to mitigating and predicting future population changes. Time series, like those used in the present study, and infrastructure with resources like the trapping facility on the river Etneelva are scarce.
Still, they are fundamental tools for studying and analyzing changes in population demography over time and among regions and are vital for the sustainable management of wild salmon populations.

ACK N OWLED G M ENTS
We acknowledge the valuable contribution from a number of professional field crew to operate the trap and conduct sampling. We thank Erlend Waatevik and local anglers for providing historical and contemporary salmon scale samples from the river Etneelva. Gunnel Østborg kindly read subsamples of salmon scales. Vidar Wennevik is acknowledged for drawing the map. The Etneelva river owner association has kindly permitted the study. Part of K.U's contribution was made possible by the NRF funded project No. 280308 SeaSalar. The study was funded by the Norwegian Ministry of Trade, Industry and Fisheries and Oppdrettsnaeringens sammenslutning for utfisking av rømt oppdrettsfisk (OURO).

CO N FLI C T O F I NTE R E S T
None declared.

DATA AVA I L A B I L I T Y S TAT E M E N T
The raw data underlying the study consist of 8188 individual salmon spawners. These, and the metadata, will be archived and